In recent decades, segmentation methods have become increasingly important in facilitating radiological and diagnostic tasks. Segmentation methods may be used to automatically identify regions of interest, such as bones or organs, in medical images acquired by various imaging modalities (e.g., magnetic resonance imaging or computed tomography). Therefore, it is not surprising that there have been a multitude of segmentation methods developed in recent years.
In spite of the availability of these segmentation methods, it is generally not easy to apply a particular method to another structure and/or imaging modality. Generic segmentation of organs in medical images is a very challenging task, due to the changing characteristics of different organs, large variations of deformable organs, strong dependence on prior knowledge, different imaging properties of multiple modalities, and many other factors. As a result, each segmentation method is typically tailored towards a specific anatomical structure (e.g., prostate or heart) and specific imaging modality (e.g., computed tomography). Key parameters in the deformable model have to be adjusted in order for the method to work in another specific application.
These manual adjustments are often very time consuming and ineffective. Therefore, there is a need to provide a more generic segmentation model that is directly applicable to different imaging modalities and different surfaces or structures, without the need to make major adjustments to parameters of the segmentation model when used in another application.